1. Why mapping in R?  

2. Spatial data & Coordinate reference system  

3. Importation & attribute manipulation of vector data with sf  

4. Importation of raster data with raster  

5. Geometry manipulations for vector and raster data  

6. Thematic & interactive maps  

7. Questions, discussion, and use of your data

R code for this map

1. Open-source, free
   • Benefit from a very active community
   • Very large number of packages

1. Open-source, free

2. Workflow and reproducibility
   • import, format, analyze, visualize, export your data
   • repeat with new data
   • create your own functions/packages

1. Open-source, free

2. Workflow and reproducibility

3. Quite efficient
   • well-defined spatial classes
   • can read/write/convert many formats

1. Open-source, free

2. Workflow and reproducibility

3. Quite efficient

4. Many interfaces to other software
   • C/C++: Geos, proj4, Gdal
   • JavaScript: Leaflet
   • otherGIS: RQGIS, rgrass7, RgoogleMaps
   • retrieve data: osmdata

See Chapter 10 of Geocomputation with R

Possible limitations

• Geo-referencing
• Visualizing large spatial objects
• Watershed analysis
• …

• Any place on Earth can be specified by a latitude and longitude or X/Y coordinates

Geographic (or unprojected) CRS

• Latitude and longitude, i.e. angles measured from the Earth’s center to a point on the Earth’s surface
• 3-D representation of Earth (sphere or ellipsoid)
• Distance in geographic CRSs are therefore measured in degrees, not meters
• Lon/Lat locate any points on Earth’s surface, but are not uniform units of measure

Projected CRS

• Uses Cartesian coordinates, Easting and Northing (x and y) typically in meters
• 2-D representation of Earth
• All projected CRSs are based on a geographic CRS
• Different mathematical formulas (i.e. projections) can transform the 3-D globe to a 2-D map

What are geographic coordinate systems?
What are projected coordinate systems?

Many, many ways to represent the 3-D shape of the earth and to project it in a 2-D plane

• each CRS can be defined either by an EPSG or a proj4string

The EPSG code is a numeric representation of a CRS, while the proj4string reprensents the full set of parameters spelled out in a string:
EPSG 4326 <=> proj4 +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs
EPSG 32188 <=> proj4 +proj=tmerc +lat_0=0 +lon_0=-73.5 +k=0.9999 +x_0=304800 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs

• All geographic files were created using a specific CRS - but it’s not always defined!

• To find CRS in any format: Spatial Reference

Why use the sf package when sp is already tried and tested?

• Simple features refers to a formal standard (ISO 19125-1:2004) that describes how objects in the real world can be represented in computers

• successor of sp

• sf incorporates the functionality of the 3 main packages of the sp paradigm in a single, cohesive whole:

  1. sp for the class system;
  2. rgdal for reading and writing data;
  3. rgeos for spatial operations undertaken by GEOS.

Why using sf while sp is still there and work just fine?

• sf objects are easy to manipulate
  • Spatial objects are stored as data frames, with the feature geometries stored in list-columns
  • Functions begin with st_ for easy tab completion
  • Functions are pipe-friendly (%>%)
  • dplyr and tidyr verbs have been defined for the sf objects
  • Reading and writing data is really simple
  • ggplot2 friendly
• GREAT documentation! See sf vignettes

sf vignette #1

• Non-spatial data
  • Water quality measures in Montreal ¹
• Vector data
  • MULTIPOINT - sample points of water quality measures in Montreal ¹
  • MULTILINESTRING - streams and rivers of Montreal ¹
  • MULTIPOLYGON - polygons of land use types ²
  • MULTIPOLYGON - Canadian boundaries (retrieved directly from R)
• Raster data
  • raster - canopy index of Montreal ²
  • raster - altitude (retrieved directly from R)

library(sf) # for simple features vector
library(lwgeom) # for st_make_valid
library(dplyr) # for data manipulation
library(reshape2) # for data manipulation
library(RColorBrewer) # for color palette
library(raster) # for raster data
library(mapview) # for interactive map

This dataset defines the localisation of the sampling points for the RUISSO program. The latter consists in analyzing the bacteriological and physicochemical quality of inland streams and watercourses in Montreal.

# Create a new directory to download data
if(!dir.exists("data")) dir.create("data")

# Download csv file from web page in your working directory
if (!file.exists("data/ruisso.csv"))
  download.file("http://donnees.ville.montreal.qc.ca/dataset/86843d31-4251-4002-b10d-620aaa751092/resource/adad6c48-fb48-40fc-a031-1ac870d978b4/download/scri03.-infor03.02-informatique03.02.07-donneesouvertesrsmaruissostationsstations_ruisso.csv",
  destfile = "data/ruisso.csv")

Portail de données ouvertes de Montréal

# Read csv file in R
ruisso <- read.csv("data/ruisso.csv", header = T, dec = ",")
names(ruisso)

## [1] "Plan.d.eau"              "Point.d.échantillonnage"
## [3] "Localisation"            "Administration"         
## [5] "LATITUDE"                "LONGITUDE"              
## [7] "Activité"

When converting from a data.frame, you have to specify the column names or numbers holding coordinates and the CRS, while for sp objects, it’s not required.

ruisso_sf <- st_as_sf(x = ruisso, coords = c("LONGITUDE", "LATITUDE"), crs = 4326) 
# the CRS is given in the metadata
head(ruisso_sf, 1)

## Simple feature collection with 1 feature and 5 fields
## geometry type:  POINT
## dimension:      XY
## bbox:           xmin: -73.93704 ymin: 45.45022 xmax: -73.93704 ymax: 45.45022
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
##         Plan.d.eau Point.d.échantillonnage
## 1 Rivière à l'Orme                 AAO-0.0
##                                                                                                Localisation
## 1 Pierrefonds, boul. Gouin O, 40m au nord de la rue de l'Anse à l'Orme, exutoire au lac des Deux Montagnes.
##        Administration Activité                   geometry
## 1 Pierrefonds-Roxboro    Actif POINT (-73.93704 45.45022)

Instead of creating a single map, as with sp object, the default plot of sf object creates multiple maps, one for each attribute, which can be useful for exploring the spatial distribution of different variables.

plot(ruisso_sf)  

To plot only the geometry and not all attributes, we retrieve the geometry list-column using st_geometry():

plot(st_geometry(ruisso_sf))

This dataset contains the actual water quality measurements at the RUISSO sampling points.

# Download csv file from web page in your working directory
if (!file.exists("data/donnees_ruisso_2016.csv"))
  download.file("http://donnees.ville.montreal.qc.ca/dataset/8c149ace-7b2f-4041-99ec-3bdbef5dcee6/resource/38c8eb26-46a1-4fc2-87a0-5c88e94cee8e/download/donnees_ruisso_2016.csv",
  destfile = "data/ruisso_data.csv")

Portail de données ouvertes de Montréal

# Read csv file in R
ruisso_data <- read.csv("data/ruisso_data.csv", header = T, dec = ",")
head(ruisso_data, 3)

##   Point.d.échantillonnage       Date Raison.d.annulation X.OD O2..mg.L.
## 1                 AAO-0.0 2016/05/12                      110      10.7
## 2                 AAO-0.0 2016/06/01                       74       7.4
## 3                 AAO-0.0 2016/06/22                       72       6.7
##   Conductivité..µs.cm2.  pH Température..oC. Signe.COLI COLI MÉTÉO
## 1                   514 7.9             16.9          <   10     1
## 2                  1434 7.9             15.0          =   54     1
## 3                  1474 7.7             19.1          =  230     0
##   Ag..µg.L. Al..µg.L. As..µg.L. Ba..µg.L. Be..µg.L. Ca..µg.L. Cd..µg.L.
## 1       0.1       214       0.4        37       0.1     44200       0.1
## 2       0.1        60       0.5        69       0.1    109000       0.1
## 3       0.1       761       0.9        58       0.1    104000       0.1
##   Co..µg.L. COT..µg.L. Cr..µg.L. Cu..µg.L. Fe..µg.L. K..µg.L. Mg..µg.L.
## 1       0.2        7.2       0.6       1.9       364     1970     15600
## 2       0.2        6.4       0.3       3.2       271     4190     37100
## 3       0.8        6.1       2.2       4.3      1200     4600     39300
##   Mn..µg.L. Mo..µg.L. Na..µg.L. NH3..µg.L. Ni..µg.L. Ptot..µg.L. Pb..µg.L.
## 1      40.4       1.5     46600         30       1.2          37       0.5
## 2      70.3       4.1    100000        160       2.3          33       0.2
## 3      82.8       4.1    100000        290       3.0         129       3.2
##   MES...mg.L. Sb..µg.L. Se..µg.L. Sn2.ug.L Tl.ug.L U..µg.L. V..µg.L.
## 1         6.8       0.5       0.5        1     0.2      0.8      0.9
## 2         5.7       0.5       0.5        1     0.2      1.7      0.6
## 3        35.3       0.5       0.5        1     0.2      1.8      2.8
##   Zn..µg.L.
## 1         7
## 2         7
## 3        12

Let’s now combine the MULTIPOINT object of sample coordinates to the water quality measurements using left_join() from dplyr, based on a shared ‘key’ variable.

See dplyr and tidyr cheatsheet for other useful functions.

ruisso_sf <- left_join(ruisso_sf, ruisso_data, by = "Point.d.échantillonnage")
names(ruisso_sf)

##  [1] "Plan.d.eau"              "Point.d.échantillonnage"
##  [3] "Localisation"            "Administration"         
##  [5] "Activité"                "Date"                   
##  [7] "Raison.d.annulation"     "X.OD"                   
##  [9] "O2..mg.L."               "Conductivité..µs.cm2."  
## [11] "pH"                      "Température..oC."       
## [13] "Signe.COLI"              "COLI"                   
## [15] "MÉTÉO"                   "Ag..µg.L."              
## [17] "Al..µg.L."               "As..µg.L."              
## [19] "Ba..µg.L."               "Be..µg.L."              
## [21] "Ca..µg.L."               "Cd..µg.L."              
## [23] "Co..µg.L."               "COT..µg.L."             
## [25] "Cr..µg.L."               "Cu..µg.L."              
## [27] "Fe..µg.L."               "K..µg.L."               
## [29] "Mg..µg.L."               "Mn..µg.L."              
## [31] "Mo..µg.L."               "Na..µg.L."              
## [33] "NH3..µg.L."              "Ni..µg.L."              
## [35] "Ptot..µg.L."             "Pb..µg.L."              
## [37] "MES...mg.L."             "Sb..µg.L."              
## [39] "Se..µg.L."               "Sn2.ug.L"               
## [41] "Tl.ug.L"                 "U..µg.L."               
## [43] "V..µg.L."                "Zn..µg.L."              
## [45] "geometry"

Keep only active sites

ruisso_sf1 <- filter(ruisso_sf, Activité == "Actif")
# same as
# ruisso_sf1 <- filter(ruisso_sf, Activité != "Inactif")
dim(ruisso_sf)

## [1] 361  45

dim(ruisso_sf1)

## [1] 345  45

Remove unwanted variables

ruisso_sf2 <- dplyr::select(ruisso_sf1,
                            -Localisation,
                            -Activité,
                            -Raison.d.annulation,
                            -Signe.COLI,
                            -MÉTÉO)

Note: raster and dplyr packages have a function select(). To avoid an error message when both packages are loaded, we explicitly use the namespace of the package : dplyr::select().

names(ruisso_sf2)

##  [1] "Plan.d.eau"              "Point.d.échantillonnage"
##  [3] "Administration"          "Date"                   
##  [5] "X.OD"                    "O2..mg.L."              
##  [7] "Conductivité..µs.cm2."   "pH"                     
##  [9] "Température..oC."        "COLI"                   
## [11] "Ag..µg.L."               "Al..µg.L."              
## [13] "As..µg.L."               "Ba..µg.L."              
## [15] "Be..µg.L."               "Ca..µg.L."              
## [17] "Cd..µg.L."               "Co..µg.L."              
## [19] "COT..µg.L."              "Cr..µg.L."              
## [21] "Cu..µg.L."               "Fe..µg.L."              
## [23] "K..µg.L."                "Mg..µg.L."              
## [25] "Mn..µg.L."               "Mo..µg.L."              
## [27] "Na..µg.L."               "NH3..µg.L."             
## [29] "Ni..µg.L."               "Ptot..µg.L."            
## [31] "Pb..µg.L."               "MES...mg.L."            
## [33] "Sb..µg.L."               "Se..µg.L."              
## [35] "Sn2.ug.L"                "Tl.ug.L"                
## [37] "U..µg.L."                "V..µg.L."               
## [39] "Zn..µg.L."               "geometry"

Rename variables

ruisso_sf3 <- rename(ruisso_sf2,
  river = Plan.d.eau,
  sample_pts = Point.d.échantillonnage,
  dissolve_O = X.OD)
names(ruisso_sf3)

##  [1] "river"                 "sample_pts"           
##  [3] "Administration"        "Date"                 
##  [5] "dissolve_O"            "O2..mg.L."            
##  [7] "Conductivité..µs.cm2." "pH"                   
##  [9] "Température..oC."      "COLI"                 
## [11] "Ag..µg.L."             "Al..µg.L."            
## [13] "As..µg.L."             "Ba..µg.L."            
## [15] "Be..µg.L."             "Ca..µg.L."            
## [17] "Cd..µg.L."             "Co..µg.L."            
## [19] "COT..µg.L."            "Cr..µg.L."            
## [21] "Cu..µg.L."             "Fe..µg.L."            
## [23] "K..µg.L."              "Mg..µg.L."            
## [25] "Mn..µg.L."             "Mo..µg.L."            
## [27] "Na..µg.L."             "NH3..µg.L."           
## [29] "Ni..µg.L."             "Ptot..µg.L."          
## [31] "Pb..µg.L."             "MES...mg.L."          
## [33] "Sb..µg.L."             "Se..µg.L."            
## [35] "Sn2.ug.L"              "Tl.ug.L"              
## [37] "U..µg.L."              "V..µg.L."             
## [39] "Zn..µg.L."             "geometry"

Remove symbols from column names:

names(ruisso_sf3) <- gsub("\\..*", "", names(ruisso_sf3))
names(ruisso_sf3)

##  [1] "river"          "sample_pts"     "Administration" "Date"          
##  [5] "dissolve_O"     "O2"             "Conductivité"   "pH"            
##  [9] "Température"    "COLI"           "Ag"             "Al"            
## [13] "As"             "Ba"             "Be"             "Ca"            
## [17] "Cd"             "Co"             "COT"            "Cr"            
## [21] "Cu"             "Fe"             "K"              "Mg"            
## [25] "Mn"             "Mo"             "Na"             "NH3"           
## [29] "Ni"             "Ptot"           "Pb"             "MES"           
## [33] "Sb"             "Se"             "Sn2"            "Tl"            
## [37] "U"              "V"              "Zn"             "geometry"

Tuto on regular expression

Remove accents from column names

names(ruisso_sf3) <- gsub("é", "e", names(ruisso_sf3))
names(ruisso_sf3)

##  [1] "river"          "sample_pts"     "Administration" "Date"          
##  [5] "dissolve_O"     "O2"             "Conductivite"   "pH"            
##  [9] "Temperature"    "COLI"           "Ag"             "Al"            
## [13] "As"             "Ba"             "Be"             "Ca"            
## [17] "Cd"             "Co"             "COT"            "Cr"            
## [21] "Cu"             "Fe"             "K"              "Mg"            
## [25] "Mn"             "Mo"             "Na"             "NH3"           
## [29] "Ni"             "Ptot"           "Pb"             "MES"           
## [33] "Sb"             "Se"             "Sn2"            "Tl"            
## [37] "U"              "V"              "Zn"             "geometry"

Tuto on regular expression

There are multiple measurements during the summer.

ruisso_sf3

## Simple feature collection with 345 features and 39 fields
## geometry type:  POINT
## dimension:      XY
## bbox:           xmin: -73.93704 ymin: 45.42516 xmax: -73.50467 ymax: 45.69734
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
## First 10 features:
##               river sample_pts      Administration       Date dissolve_O
## 1  Rivière à l'Orme    AAO-0.0 Pierrefonds-Roxboro 2016/05/12      110.0
## 2  Rivière à l'Orme    AAO-0.0 Pierrefonds-Roxboro 2016/06/01       74.0
## 3  Rivière à l'Orme    AAO-0.0 Pierrefonds-Roxboro 2016/06/22       72.0
## 4  Rivière à l'Orme    AAO-0.0 Pierrefonds-Roxboro 2016/08/02       79.0
## 5  Rivière à l'Orme    AAO-0.0 Pierrefonds-Roxboro 2016/08/22       79.0
## 6  Rivière à l'Orme    AAO-0.0 Pierrefonds-Roxboro 2016/09/20       76.8
## 7  Rivière à l'Orme    AAO-0.0 Pierrefonds-Roxboro 2016/10/25       96.9
## 8  Rivière à l'Orme  AAO-3.3P6            Kirkland 2016/05/12      110.0
## 9  Rivière à l'Orme  AAO-3.3P6            Kirkland 2016/06/01       81.0
## 10 Rivière à l'Orme  AAO-3.3P6            Kirkland 2016/06/22       93.0
##      O2 Conductivite  pH Temperature  COLI  Ag   Al  As Ba  Be     Ca  Cd
## 1  10.7          514 7.9        16.9    10 0.1  214 0.4 37 0.1  44200 0.1
## 2   7.4         1434 7.9        15.0    54 0.1   60 0.5 69 0.1 109000 0.1
## 3   6.7         1474 7.7        19.1   230 0.1  761 0.9 58 0.1 104000 0.1
## 4   6.8         1346 7.9        22.3   300 0.1  214 0.8 67 0.1  98200 0.1
## 5   7.3          793 7.8        19.1   550 0.1  353 0.5 56 0.1  71200 0.1
## 6   7.1         1286 7.7        18.8    72 0.1  207 0.5 61 0.1 102000 0.1
## 7  11.0         1414 7.7         9.4    99 0.1  259 0.4 77 0.1 122000 0.1
## 8  11.6         1589 7.9        12.9  1200 0.1   81 0.3 60 0.1 125000 0.1
## 9   8.1         1639 8.0        15.6  4400 0.1   95 0.4 56 0.1 123000 0.1
## 10  9.2          897 7.9        15.6 33000 0.1 2600 1.1 63 0.1  72700 0.1
##     Co  COT  Cr   Cu   Fe    K    Mg    Mn  Mo     Na NH3  Ni Ptot  Pb
## 1  0.2  7.2 0.6  1.9  364 1970 15600  40.4 1.5  46600  30 1.2   37 0.5
## 2  0.2  6.4 0.3  3.2  271 4190 37100  70.3 4.1 100000 160 2.3   33 0.2
## 3  0.8  6.1 2.2  4.3 1200 4600 39300  82.8 4.1 100000 290 3.0  129 3.2
## 4  0.3 15.1 0.5  1.6  393 4180 36000  65.1 3.9 100000  90 2.0   67 0.9
## 5  0.4  4.3 1.3  3.0  533 3150 19500  44.2 2.9  58200 120 2.3   55 0.8
## 6  0.2  5.3 0.7  3.2  367 4100 33700  18.9 3.3 100000  40 2.7   31 0.7
## 7  0.3  4.2 0.9  3.1  422 5590 35900  24.7 3.8 100000  70 2.4   36 0.6
## 8  0.2  3.8 0.3  3.3  237 4070 36300  57.2 2.3 100000  60 2.0   23 0.2
## 9  0.2  3.7 0.3  8.0  258 4840 35400  68.3 3.1 100000 100 2.1  207 0.2
## 10 2.7 23.6 9.9 30.0 3950 4790 20900 261.0 2.6  92300 490 7.6  430 5.6
##      MES  Sb  Se Sn2  Tl   U   V  Zn                   geometry
## 1    6.8 0.5 0.5   1 0.2 0.8 0.9   7 POINT (-73.93704 45.45022)
## 2    5.7 0.5 0.5   1 0.2 1.7 0.6   7 POINT (-73.93704 45.45022)
## 3   35.3 0.5 0.5   1 0.2 1.8 2.8  12 POINT (-73.93704 45.45022)
## 4    9.0 0.5 0.5   1 0.2 1.8 1.7   7 POINT (-73.93704 45.45022)
## 5   10.3 0.5 0.5   1 0.2 1.2 1.7   7 POINT (-73.93704 45.45022)
## 6    3.6 0.5 0.5   1 0.2 1.3 1.2   7 POINT (-73.93704 45.45022)
## 7    5.8 0.5 0.5   1 0.2 2.2 1.2   7 POINT (-73.93704 45.45022)
## 8    3.4 0.5 0.5   1 0.2 1.9 0.7   7 POINT (-73.90147 45.43689)
## 9    5.0 0.5 0.5   1 0.2 1.6 1.0   7 POINT (-73.90147 45.43689)
## 10 161.0 1.2 0.5   1 0.2 0.9 9.4 108 POINT (-73.90147 45.43689)

We will use the mean of water quality measurements. We could have used the max or min or chosen a single month of interest.

ruisso_mean <- ruisso_sf3 %>%
  group_by(sample_pts) %>% # split the data into groups of samples
  summarise_at(vars(O2:Zn), mean, na.rm = TRUE) # mean by group on selected variables
ruisso_mean

## Simple feature collection with 50 features and 35 fields
## geometry type:  POINT
## dimension:      XY
## bbox:           xmin: -73.93704 ymin: 45.42516 xmax: -73.50467 ymax: 45.69734
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
## # A tibble: 50 x 36
##    sample_pts    O2 Conductivite    pH Temperature   COLI    Ag    Al    As
##    <chr>      <dbl>        <dbl> <dbl>       <dbl>  <dbl> <dbl> <dbl> <dbl>
##  1 AAO-0.0     8.14        1180.  7.8         17.2 1.88e2   0.1 295.  0.571
##  2 AAO-3.3P6   9.5         1378.  7.89        16.4 1.86e4   0.1 432.  0.457
##  3 AAO-3.5     8.79        1281.  7.74        14.3 6.12e2   0.1 150.  0.629
##  4 AAO-3.6     9           1128.  7.97        16.1 4.61e2   0.1 217.  0.429
##  5 AAO-6.4P12 10.1         1461.  7.96        18.2 1.47e2   0.1  33.1 0.243
##  6 AAO-6.5     7.4          567.  8.12        16.5 1.22e3   0.1 109.  0.7  
##  7 ADM-1       9.97         333.  8.19        21.2 5.86e1   0.1 119   1.11 
##  8 ANG-2       9.7          399.  8.33        20.2 4.10e1   0.1  60.7 1.3  
##  9 BER-0.0     7.18         803.  7.61        17.1 3.05e2   0.1 140.  0.457
## 10 BER-0.7P1   9.25         751.  7.76        17.1 1.43e3   0.1  75.7 0.529
## # … with 40 more rows, and 27 more variables: Ba <dbl>, Be <dbl>,
## #   Ca <dbl>, Cd <dbl>, Co <dbl>, COT <dbl>, Cr <dbl>, Cu <dbl>, Fe <dbl>,
## #   K <dbl>, Mg <dbl>, Mn <dbl>, Mo <dbl>, Na <dbl>, NH3 <dbl>, Ni <dbl>,
## #   Ptot <dbl>, Pb <dbl>, MES <dbl>, Sb <dbl>, Se <dbl>, Sn2 <dbl>,
## #   Tl <dbl>, U <dbl>, V <dbl>, Zn <dbl>, geometry <POINT [°]>

plot(ruisso_mean)  

myPal <- colorRampPalette(c("blue", "red"))
plot(ruisso_mean["Temperature"],
  pal = myPal, nbreaks = 30, pch = 19, key.pos = 1,
  main = "Water temperature in streams of Montreal")

We can write a simple features object to a file (e.g. a shapefile) using the st_write() function in sf (see vignette #2)

st_write(ruisso_mean, dsn = "data/ruisso.shp", driver = "ESRI Shapefile", delete_dsn = T)

## Warning in abbreviate_shapefile_names(obj): Field names abbreviated for
## ESRI Shapefile driver

Some argument specifications depend on the driver, see ?st_write

We will now import a land use vector map of Montreal.

Observatoire du Grand Montréal

The land use dataset is composed of multiple individual shapefiles, the following manipulations allow to download, unzip all the individual files, combine them and save the resulting file as a GeoPackage. These manipulations can take a few minutes and should have been performed before the workshop using this R script.

Don’t run these lines now!

# Download shapefiles
download.file("http://cmm.qc.ca/fileadmin/user_upload/geomatique/UtilisationDuSol/2016_Shapefiles/660-US-2016.zip", destfile = "data/landuse.zip")

# Unzip the main folder and name it "landuse"
unzip("landuse.zip", exdir="data/landuse")

# get all the zip files inside the main folder "landuse"
zipF <- list.files(path = "data/landuse/", pattern = "*.zip", full.names = TRUE)

# unzip all your files
plyr::ldply(.data = zipF, .fun = unzip, exdir = "data/landuse")

Don’t run these lines now!

# Get the names of the land use shapefiles from the folder "landuse"
shp_files <- list.files(path = "data/landuse", pattern = ".shp")
shp_files <- sub(".shp", "", shp_files)

# Read all the shapefiles
LU <- list()
for(f in shp_files) {
  LU[[f]] <- st_read(dsn = "data/landuse/", layer = f)
}

# Combine all shapefiles together
LU_all <- do.call(rbind, LU)

# Write as a GeoPackage
st_write(LU_all, "data/LU_all.gpkg", driver = "GPKG")

# Read GeoPackage in R
LU <- st_read(dsn = "data/LU_all.gpkg")

## Reading layer `LU_all' from data source `/Users/mariehbrice/Documents/GitHub/Rspatial/docs/data/LU_all.gpkg' using driver `GPKG'
## Simple feature collection with 107233 features and 38 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 265961.2 ymin: 5027324 xmax: 306814.6 ymax: 5063078
## epsg (SRID):    NA
## proj4string:    +proj=tmerc +lat_0=0 +lon_0=-73.5 +k=0.9999 +x_0=304800 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs

Notice that the EPSG code is not defined

To define the CRS when it’s missing we can use st_set_crs().

Note however that replacing crs does not re-project data. If we wanted to transform coordinate and re-project them, we would use st_transform() .

LU <- st_set_crs(LU, 32188)

sf vignette #3

We can map only one land use at a time by subsetting first the sf object. UTIL_SOL==1000 represents water. Don’t try to plot everything… it would take forever!

plot(st_geometry(subset(LU, UTIL_SOL==1000)))

Geometry validity refers to essential properties of polygons, such as:

• Polygon rings must close.
• Rings that define holes should be inside rings that define exterior boundaries.
• Rings may not self-intersect (they may neither touch nor cross one another).
• Rings may not touch other rings, except at a point.

Invalid geometry can be ignored for mapping but cause problem for many spatial operations.

st_is_valid() returns a logical vector indicating for each polygon geometries indicating whether it is topologically valid:

head(invalid_poly <- which(!st_is_valid(LU)))

## [1]  862 1149 1251 1406 2071 2119

Using the argument reason = TRUE returns the reason for invalidity:

st_is_valid(LU[invalid_poly,], reason = TRUE)[1:8]

## [1] "Nested shells[295499.0604 5045532.3412]"    
## [2] "Nested shells[298395.5742 5045168.6803]"    
## [3] "Self-intersection[297511.205 5032954.7058]" 
## [4] "Nested shells[273102.34356 5035592.911633]" 
## [5] "Nested shells[274053.456247 5036041.908298]"
## [6] "Nested shells[288581.300144 5036823.875472]"
## [7] "Self-intersection[294414.9683 5031596.2768]"
## [8] "Self-intersection[295265.9577 5045076.9297]"

par(mfrow = c(1,2))
plot(st_geometry(LU[862,]), main = "Nested shells")

plot(st_geometry(LU[1251,]), main = "Self-intersection")

We can use st_make_valid() from lwgeom to make an invalid geometry valid

LU_val <- st_make_valid(LU)

# let's verify if all geometries are now valid
which(!st_is_valid(LU_val)) # yeah!

## integer(0)

1. Import a shapefile of watercourses in Montreal (courseau.shp) using st_read(dsn = path_to_file, layer = file_name, driver = "ESRI Shapefile") and name it courseau

If you did not load the shapefile before the workshop you can download it using download.file() from this link : http://donnees.ville.montreal.qc.ca/dataset/c128aff5-325c-4599-ab66-1c9d0b3abc94/resource/a37e11d4-f0a3-46a7-8636-76754fad72b3/download/courseau.zip

2. Create a simple map of the geometry

3. Create a simple thematic map of watercourse TYPE. You can change the default colors using the argument pal.

# Download shapefile from web page in your working directory
if (!file.exists("data/courseau.zip"))
  download.file("http://donnees.ville.montreal.qc.ca/dataset/c128aff5-325c-4599-ab66-1c9d0b3abc94/resource/a37e11d4-f0a3-46a7-8636-76754fad72b3/download/courseau.zip", destfile = "data/courseau.zip")

# Unzip the main folder
unzip("data/courseau.zip", exdir = "data/courseau")

# For shapefiles, dsn may be the character string holding the geojson data
courseau <- st_read(dsn = "data/courseau/", layer = "courseau", quiet = T)

courseau

## Simple feature collection with 1306 features and 5 fields
## geometry type:  MULTILINESTRING
## dimension:      XY
## bbox:           xmin: -73.97268 ymin: 45.41593 xmax: -73.4975 ymax: 45.69939
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
## First 10 features:
##    OBJECTID_1              NOM     TYPE Shape_Le_1 NuméroRui
## 1           1 rivière à l'Orme  rivière  177.95299      <NA>
## 2           2 rivière à l'Orme  rivière  128.51146      <NA>
## 3           3             <NA>    fossé  172.42988      <NA>
## 4           4 rivière à l'Orme  rivière  216.66838      <NA>
## 5           8             <NA>    fossé  540.29539      <NA>
## 6           9             <NA> ruisseau   97.66412      <NA>
## 7          10             <NA> ruisseau  162.30584      <NA>
## 8          11             <NA> ruisseau  210.75794      <NA>
## 9          14             <NA> ruisseau  608.28651      <NA>
## 10         16             <NA>    fossé  267.61108      <NA>
##                          geometry
## 1  MULTILINESTRING ((-73.9107 ...
## 2  MULTILINESTRING ((-73.90824...
## 3  MULTILINESTRING ((-73.90667...
## 4  MULTILINESTRING ((-73.91472...
## 5  MULTILINESTRING ((-73.91029...
## 6  MULTILINESTRING ((-73.9206 ...
## 7  MULTILINESTRING ((-73.91969...
## 8  MULTILINESTRING ((-73.91727...
## 9  MULTILINESTRING ((-73.91727...
## 10 MULTILINESTRING ((-73.91212...

par(mar=c(0,0,0,0))
plot(st_geometry(courseau))

par(mar=c(0,0,0,0))
plot(courseau["TYPE"], key.pos = 1, pal = brewer.pal(4, "Paired"))

raster provides three main classes of raster object:

RasterLayer - a single-layer (variable) raster (e.g. elevation)

RasterStack - one single object several single-layer (variable) rasters stored in one or different files (e.g. Worldclim bioclimatic variables)

Raster data

We now import raster data use a .tif file of a canopy index from Montreal.

# Download tif file from web page in your working directory
if (!file.exists("data/canopee.zip")){
  download.file("http://cmm.qc.ca/fileadmin/user_upload/geomatique/IndiceCanopee/2015/660_ Canopee2015_3m.zip", destfile = "data/canopee.zip") }

unzip("data/canopee.zip", exdir = "data")

# Read tif in R using raster
# The file named "660_CLASS_3m.tif" contains the canopy index for all the Montreal area, so we can read this file only
canopee_mtl <- raster("data/660_CLASS_3m.tif")

Observatoire du Grand Montréal

canopee_mtl

## class       : RasterLayer 
## dimensions  : 35754, 40854, 1460693916  (nrow, ncol, ncell)
## resolution  : 1, 1  (x, y)
## extent      : 265961, 306815, 5027324, 5063078  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=tmerc +lat_0=0 +lon_0=-73.5 +k=0.9999 +x_0=304800 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs 
## data source : /Users/mariehbrice/Documents/GitHub/Rspatial/docs/data/660_CLASS_3m.tif 
## names       : X660_CLASS_3m 
## values      : 1, 5  (min, max)

The canopy index dataset is a RasterLayer with values from 1 to 5, 35754 pixels by row and 40854 pixels by column and resolution of 1m x 1m.

Similar to the sf package, raster also provides plot methods for its own classes.

plot(canopee_mtl)

In package raster, getData() function generates requests to access to different spatial datasets (either raster or sp objects). Argument name specifies the dataset you wish to download.

GADM - global administrative boundaries at different level of administrative subdivision
worldclim - global interpolated climate data
alt and STRM - coarse and fine resolution elevation data
ISO3 - 3 letter ISO codes for country names.

head(getData("ISO3"))

##   ISO3                  NAME
## 1  AFG           Afghanistan
## 2  XAD Akrotiri and Dhekelia
## 3  ALA                 Åland
## 4  ALB               Albania
## 5  DZA               Algeria
## 6  ASM        American Samoa

Retrieve a raster of altitude for Canada.

# alt90 <- getData('SRTM', lon = -73.7, lat = 45.5) # Fine resolution
altCAN <- getData(name = "alt", country = "CAN", path = "data/") # Coarse resolution
altCAN

## class       : RasterLayer 
## dimensions  : 4992, 10620, 53015040  (nrow, ncol, ncell)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : -141.1, -52.6, 41.6, 83.2  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0 
## data source : /Users/mariehbrice/Documents/GitHub/Rspatial/docs/data/CAN_msk_alt.grd 
## names       : CAN_msk_alt 
## values      : -108, 5800  (min, max)

Because the resolution of the altitude raster (0.0083x0.0083° = 650x926m) is a lot coarser than that of the canopy index (1x1m), we will use the altitude raster for examples of raster manipulations to reduce computation time.

Retrieve a raster of altitude for Canada.

plot(altCAN)

1. use getData() to retrieve Canadian boundary map at the provincial level
   • ?getData

2. Transform it to a sf object using st_as_sf()

3. Subset the Quebec boundary in the column NAME_1

4. Re-project your new object using the same projection that of the land use polygons (st_crs(LU_val) or with the EPSG code 32188)

5. Try to plot the geometry of the Quebec polygon.

6. Try qc_simple_prj <- st_simplify(qc_prj, dTolerance = 500, preserveTopology = F) and plot the geometry of this new object. Is there a difference?

# get canada boundary map
can <- raster::getData("GADM", country = "CAN", level = 1, path = "data/")
# convert to sf
can <- st_as_sf(can)
# Subset Quebec
qc <- can[can$NAME_1 == "Québec",]
# transform projection
qc_prj <- st_transform(qc, 32188)
# simplify the polygons so it's lighter
qc_simple_prj <- st_simplify(qc_prj, dTolerance = 500, preserveTopology = F)

plot(st_geometry(qc_simple_prj))

ruisso_buf <- st_buffer(st_geometry(ruisso_mean), dist = 0.01)

## Warning in st_buffer.sfc(st_geometry(ruisso_mean), dist = 0.01): st_buffer
## does not correctly buffer longitude/latitude data

## dist is assumed to be in decimal degrees (arc_degrees).

dist() is assumed to be in decimal degrees This message indicates that sf assumes a distance value is given in degrees

st_buffer() does not correctly buffer longitude/latitude data This warning indicates that the result may be incorrect because st_buffer expects coordinates in a 2-D, Euclidean space, which is not the case for longitude latitude data. So we should re-project the data onto a projected CRS.

plot(st_geometry(ruisso_mean), pch = 19, cex= .8)
plot(ruisso_buf, add = T, border= "blue3")

We can re-project the sample points using a suitable projection that has units of meters. To do this, we will use the projection from our land use polygons.

(ruisso_proj <- st_transform(ruisso_mean, crs = st_crs(LU_val)))

## Simple feature collection with 50 features and 35 fields
## geometry type:  POINT
## dimension:      XY
## bbox:           xmin: 270615.3 ymin: 5031758 xmax: 304436.2 ymax: 5061940
## epsg (SRID):    32188
## proj4string:    +proj=tmerc +lat_0=0 +lon_0=-73.5 +k=0.9999 +x_0=304800 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## # A tibble: 50 x 36
##    sample_pts    O2 Conductivite    pH Temperature   COLI    Ag    Al    As
##    <chr>      <dbl>        <dbl> <dbl>       <dbl>  <dbl> <dbl> <dbl> <dbl>
##  1 AAO-0.0     8.14        1180.  7.8         17.2 1.88e2   0.1 295.  0.571
##  2 AAO-3.3P6   9.5         1378.  7.89        16.4 1.86e4   0.1 432.  0.457
##  3 AAO-3.5     8.79        1281.  7.74        14.3 6.12e2   0.1 150.  0.629
##  4 AAO-3.6     9           1128.  7.97        16.1 4.61e2   0.1 217.  0.429
##  5 AAO-6.4P12 10.1         1461.  7.96        18.2 1.47e2   0.1  33.1 0.243
##  6 AAO-6.5     7.4          567.  8.12        16.5 1.22e3   0.1 109.  0.7  
##  7 ADM-1       9.97         333.  8.19        21.2 5.86e1   0.1 119   1.11 
##  8 ANG-2       9.7          399.  8.33        20.2 4.10e1   0.1  60.7 1.3  
##  9 BER-0.0     7.18         803.  7.61        17.1 3.05e2   0.1 140.  0.457
## 10 BER-0.7P1   9.25         751.  7.76        17.1 1.43e3   0.1  75.7 0.529
## # … with 40 more rows, and 27 more variables: Ba <dbl>, Be <dbl>,
## #   Ca <dbl>, Cd <dbl>, Co <dbl>, COT <dbl>, Cr <dbl>, Cu <dbl>, Fe <dbl>,
## #   K <dbl>, Mg <dbl>, Mn <dbl>, Mo <dbl>, Na <dbl>, NH3 <dbl>, Ni <dbl>,
## #   Ptot <dbl>, Pb <dbl>, MES <dbl>, Sb <dbl>, Se <dbl>, Sn2 <dbl>,
## #   Tl <dbl>, U <dbl>, V <dbl>, Zn <dbl>, geometry <POINT [m]>

ruisso_buf <- st_buffer(st_geometry(ruisso_proj), dist = 500)
# add an attribute for sample points id
ruisso_buf <- st_sf(sample_pts = ruisso_mean$sample_pts, ruisso_buf)

plot(st_geometry(ruisso_proj), pch = 19, cex= .5)
plot(st_geometry(ruisso_buf), add = T, border= "blue3")

1. Re-project the courseau object using the land use projection and name it courseau_proj

2. Create a 300m buffer around each watercourse and name it courseau_buf. Plot the resulting geometry with courseau_proj on top.

3. Try st_union() on courseau_buf. Plot the resulting geometry and compare with courseau_buf.

4. Try st_centroid() on ruisso_buf. Plot the resulting geometry with ruisso_buf under.

For more geometric operations such as st_distance(), st_convex_hull(), st_intersection() see sf vignette #3

# transform projection
courseau_proj <- st_transform(courseau, crs = st_crs(LU_val))
# create buffer
courseau_buf <- st_buffer(st_geometry(courseau_proj), dist = 300)
# add an unique ID to your buffer
courseau_buf <- st_sf(ID = courseau_proj$OBJECTID_1, courseau_buf)

plot(st_geometry(courseau_buf), col=NA, border="black", main = "st_buffer")
plot(courseau_proj["TYPE"], lwd = 1.2, add = T)

# compute buffer
buf_union <- st_union(courseau_buf)
plot(st_geometry(courseau_buf), col=NA, border="black", main = "st_buffer")
plot(st_geometry(buf_union), col=NA, border="black", main = "st_union")

# compute centroid
ruisso_centroid <- st_centroid(ruisso_buf)
plot(st_geometry(ruisso_buf), col=NA, border="black", main = "st_centroid")
plot(st_geometry(ruisso_centroid), pch = 19, col="red", add = T)

## Warning in st_centroid.sf(ruisso_buf): st_centroid assumes attributes are
## constant over geometries of x

LU_reclas <- LU_val %>%
  dplyr::select(ID, UTIL_SOL) %>%
  mutate(UTIL_SOL = case_when(UTIL_SOL %in% c(100:114) ~ "resid",
                               UTIL_SOL %in% c(200:520) ~ "public_build",
                               UTIL_SOL == 600 ~ "green",
                               UTIL_SOL %in% c(700:760) ~ "road",
                               UTIL_SOL == 800 ~ "agri",
                               UTIL_SOL == 900 ~ "vacant",
                               UTIL_SOL == 1000 ~ "water",
                               UTIL_SOL == 1100 ~ "golf"))

Classification and color codes of land use types

case_when allows you to vectorize multiple if and else if statements

Now we aim at finding the proportion of area per buffer occupied by different land-use types. st_intersection() can be used to obtain the intersection of two geometries (i.e. the area covered by both).

LU_inters <- st_intersection(LU_reclas, ruisso_buf)

## Warning: attribute variables are assumed to be spatially constant
## throughout all geometries

Warning: attribute variables are assumed to be spatially constant Attribute values are assigned to sub-geometries; if these are spatially constant, as for instance for land use, then this is fine. If they are aggregates, such as population count, then this is wrong.

plot(LU_inters["UTIL_SOL"], key.pos = 1)

# add in a column and compute area (area of each land use poly in intersect layer)
LU_inters$areaLU <- st_area(LU_inters)

# group data by sample points and land use type and calculate the total area for each land use per buffer
LU_area <- LU_inters %>%
  group_by(sample_pts, UTIL_SOL) %>%
  summarise(areaLU = sum(areaLU))

# remove geometry
st_geometry(LU_area) <- NULL
head(LU_area)

## # A tibble: 6 x 3
## # Groups:   sample_pts [1]
##   sample_pts UTIL_SOL     areaLU          
##   <fct>      <chr>        <S3: units>     
## 1 AAO-0.0    agri           1211.933 [m^2]
## 2 AAO-0.0    green        353192.022 [m^2]
## 3 AAO-0.0    public_build   5908.185 [m^2]
## 4 AAO-0.0    resid         29445.815 [m^2]
## 5 AAO-0.0    road          38497.788 [m^2]
## 6 AAO-0.0    vacant       110014.028 [m^2]

# buffer with 500m radius so area is pi*500^2
# try st_area(ruisso_buf)

# compute proportion for each land use
LU_area$propLU <- as.numeric(LU_area$areaLU/(pi*500^2))

# transform to wide format and create a new data frame containing our new landscape variables
env_var <- dcast(data = LU_area, formula = sample_pts ~ UTIL_SOL, value.var = "propLU", fill = 0)

Reshape data with melt and cast

crop() and mask() reduce object memory use and therefore computational time for raster manipulations.

crop() will decrease the extent of a raster, mask() will set to NA values outside a polygon boundary.

Let’s crop and mask the altitude raster using the buffer MULTIPOLYGON

# we have to reproject ruisso_buf to fit the raster and then convert it to a sp object
alt_crop <- crop(altCAN, as(st_transform(ruisso_buf, 4326), "Spatial")) 
alt_mask <- mask(alt_crop, st_transform(ruisso_buf, 4326))

You could try to crop() and mask() the altitude raster using the Quebec polygon.

par(mfrow=c(1,3))
plot(altCAN)
plot(alt_crop)
plot(alt_mask)
plot(st_geometry(ruisso_buf), add = T)

The projection of alt_crop is in longitude/latitude.

alt_crop

## class       : RasterLayer 
## dimensions  : 34, 53, 1802  (nrow, ncol, ncell)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : -73.94167, -73.5, 45.41667, 45.7  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0 
## data source : in memory
## names       : CAN_msk_alt 
## values      : 0, 179  (min, max)

We can use projectRaster() to transform the CRS of one spatial object to match another spatial object

st_crs(LU_val)[[2]] # to retrieve the proj4string

## [1] "+proj=tmerc +lat_0=0 +lon_0=-73.5 +k=0.9999 +x_0=304800 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"

alt_proj <- projectRaster(alt_crop, crs = st_crs(LU_val)[[2]])
alt_proj

## class       : RasterLayer 
## dimensions  : 40, 63, 2520  (nrow, ncol, ncell)
## resolution  : 651, 926  (x, y)
## extent      : 266977.8, 307990.8, 5028068, 5065108  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=tmerc +lat_0=0 +lon_0=-73.5 +k=0.9999 +x_0=304800 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs 
## data source : in memory
## names       : CAN_msk_alt 
## values      : 0.8431883, 174.3307  (min, max)

Compute the mean altitude per buffer

sample_alt <- raster::extract(alt_proj, as(ruisso_buf, "Spatial"), fun=mean, na.rm=TRUE)
head(sample_alt)

##          [,1]
## [1,] 23.44361
## [2,] 28.08543
## [3,] 28.08543
## [4,] 28.12213
## [5,] 30.03564
## [6,] 30.03564

# Add this new environmental variables to our data frame
env_var <- cbind.data.frame(env_var, altitude = sample_alt)

See the package velox for faster raster extraction

Now that we have a clean data frame with water quality variables (mean measurements of water quality in different watercourses) and a data frame of landscape variables of interest (% of different land use types in a 500m buffer and mean altitude in a 500m buffer), we could perform various statistical analyses.

For instance, we could run a Redundancy Analysis (RDA) to study the influence of the landscape on water quality. If we had data on macroinvertebrate abundance, we could study the relative importance of the landscape and water quality on their distribution using variation partitioning on RDA.

plot() allows combination of multiple layers of information in a same graph using add = T

plot(canopee_mtl)
plot(st_geometry(courseau_proj), col = "#034ebf", lwd = 1.5, add = T)
plot(st_geometry(ruisso_proj), pch = 19, col = "#B00C0C", add = T)

Change the color palette for the raster levels. There are 5 levels and only 2 are pertinent:

• 3 = low vegetation cover
• 4 = canopy

myPal <- c("white", "white", "#BAE4B3", "#006D2C", "white") # color palette

plot(canopee_mtl, col = myPal, legend = F) # canopy raster
plot(st_geometry(subset(LU_val, UTIL_SOL==1000)), # UTIL_SOL==1000 -> rivers
     col = "#7eb0fc", border = "#7eb0fc",add=T)
plot(st_geometry(courseau_proj), col = "#034ebf", lwd = 1.5, add = T) # watercourse
plot(st_geometry(ruisso_proj), pch = 19, col = "#B00C0C", add = T) # sample points
legend("bottomright", legend = c("Low vegetation", "Canopy"),  # legend
       fill = c("#BAE4B3", "#006D2C"), border="white", bty = "n")

Add an inset

par(mar=c(3,3,2,0))

plot(canopee_mtl, col = myPal, legend = F) # canopy raster
plot(st_geometry(subset(LU_val, UTIL_SOL == 1000)), # UTIL_SOL==1000 -> rivers
     col = "#7eb0fc", border = "#7eb0fc", add = T)
plot(st_geometry(courseau_proj), col = "#034ebf", lwd = 1.5, add = T) # watercourse
plot(st_geometry(ruisso_proj), pch = 19, col = "#B00C0C", add = T) # sample points
legend("bottomright", legend = c("Low vegetation", "Canopy"),  # legend
       fill = c("#BAE4B3", "#006D2C"), border="white", bty = "n")

par(fig = c(0.08, 0.6, 0.42, 1), new = T) # add inset at specified coordinates of the figure region

plot(st_geometry(qc_simple_prj), col = "grey85", bgc = "transparent") # Quebec polygon
points(286543, 5038936, pch = 17, col = "#B00C0C") # point on Montreal

Join our environmental variables data frame as attributes to the sf sample points

ruisso_env <- left_join(ruisso_proj, env_var, by = "sample_pts")

## Warning: Column `sample_pts` joining character vector and factor, coercing
## into character vector

mapview(ruisso_env, zcol = 'COLI', legend=TRUE, layer.name = "E. coli density")

mapview(ruisso_env, cex = "public_build", map.types = "Esri.WorldImagery")

myPal <- colorRampPalette(brewer.pal(9, "YlGnBu"))
mapview(ruisso_env, zcol = "resid", cex = "pH", legend = TRUE, col.regions = myPal,
        layer.name = "% of residential area")

R script for this plotly map

Geocomputation with R